How to extract data from your paper for systemic review - Pubrica
                     HOW TO EXTRACT DATA 
 FROM YOUR PAPER 
FOR  SYSTEMIC REVIEW
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, 
Pubrica  Group:
www.pubrica.com
Email: [email protected]
Today's 
Discussion
In-Brief
Outlin
e Introduction
Work Flow and Study 
Design  Eligibility Criteria
Key Items for Data 
Extraction  Limitations
Future Scopes 
 Conclusion
In-
Brief
Data should be extracted based on previously identified interventions and  
outcomes developed during the formulation of the study topic, inclusion/exclusion  
requirements, and search procedure. It should not be challenging to classify the  
data elements that need to be retrieved from each included sample if those 
phases  have been completed properly. To analyze and assess findings, extract 
data from  related studies. It is important to use sound data collection techniques 
when the  data is being collected (1). Data processing can begin as soon as you 
begin  collecting data, and it can even determine which data types you retain.
Introductio
n  Researchers in evidence-based medicine are  
overwhelmed by the volume of primary research  
papers, both old and modern.
Since it is currently impractical to scan for  
appropriate data with accuracy, support for the early 
 stages of the systematic review phase – searching  
and screening studies for eligibility – is needed.
Not only could better automatic data extraction help  
with the stage of analysis known as "data  
extraction," but it could also help with other aspects  
of the review process.
Contd...
Systematic review (semi)automation research lies at the intersection
between  evidence-based medicine and computer science.
Besides the advancement in computing power and storage space, computers' capacity 
 to serve humans grows.
D ata extraction for systematic analysis is a time-consuming 
process.  It opens up possibilities for sophisticated machines to 
assist.
In this domain, tools and methods are often based on automating data
processing  relevant to the PICO framework (Population, Intervention, Comparator, 
and Outcome).
Contd...
A summary of  
included 
extraction  
methods and their  
evaluation
Work 
Two critics will separately screen both titles and  
abstracts. Any discrepancies in judgement would Flow  and 
be  addressed and,  if possible, overcome with the  Study  
assistance of a third reviewer.
Design
The evaluation process for complete texts would be 
 the same, a single reviewer will extract data, and a 
 random 10% selection from each reviewer will be  
reviewed separately.
We plan to contact the writers of reports for  
confirmation or additional material if necessary.
Contd...
We will provide a cross-sectional overview of the data from our searches in the case  
study and any published update.
The analysis will include the features of each reviewed method or tool, as well as a  
summary of our outcomes.
In addition, we will evaluate the quality of reporting at the publication level.
Contd...

Eligibility 
Criteria
1. E ligible papers
Full-text articles describing  an initial natural  
language processing method to extract data for  
structured reviewing activities will be included.
The Extended data contains data areas  of  
concern adapted from the Cochrane Handbook  
for Systematic Reviews of Interventions.
Contd...
The whole spectrum of natural language processing (NLP) techniques includes regular 
 expressions, rule-based structures, machine learning, and deep artificial networks.
Papers must detail the whole process of implementing and evaluating a system.
The data used for mining in the included articles must be abstracts, conference  
proceedings, full texts, or portions of full texts from randomized clinical experiments,  
comparative cohort studies, or case management articles in the form of abstracts,  
conference proceedings, full texts, or parts of full texts.
Contd...
2. I neligible papers
We will exclude papers reporting:
Image editing and downloading biomedical data from PDF files without the
use of  natural language processing (NLP), including data retrieval from 
graphs;
Any study that focuses merely on protocol planning, synthesis of previously extracted  
data, write-up, text pre-processing, and dissemination will be disqualified;
Contd...
Methods or tools that do not use natural language processing and instead focus on  
administrative interfaces, document storage, databases, or version control; or
All articles relating to electronic health records or genomic data mining may
be  disqualified.
Key Items 
  for Data  
Primar
y Machine learning approaches Extractio
used n
used
Reported performance for
metrics  evaluation
Type of data
Scope: text, abstract, or
conference
full  
proceeding Contd..
s .
Study type: randomized clinical experiment,  
cohort, and case-control
Input data format: For example,
imported as standardized data
rlietesrualtusre searches (e.g. RIS), APIs, or datao f 
imported from PDF or text files.
Output format: The format in which the data 
 is exported after extraction is a text file.
Contd...
 Secondary:
Data granularity: Does the
mretirnieinvge individmualc heinetities, words, or whole  
sections of text?
Other indicators that have been published, such 
 as the effect on s ystemic review processes 
(e.g.  time saved during data extraction).
Limitation
s
First, there's a chance that data extraction algorithms  
haven't been published in journals or that our search  
has missed them.
We searched several bibliographic databases,  
including PubMed, IEEExplore, and the ACM Digital  
Library, to overcome this limit.
Contd...
Second, we did not publish a protocol ahead of time, and our preliminary results 
may  have affected our procedures.
To eliminate potential bias in our systematic analysis, we duplicated main steps 
such  as sampling, full-text review, and data extraction.
Future 
Scopes
According to a s ystematic analysis, information  
retrieval technology positively affects physicians in  
decision-making—the need for new methods to  
report on and  searching for organized data in  
written literature.
The use of an automated knowledge extraction
process to retrieve data elements can
 comprehensive reviewers and, in aid  long
tshimeplify the searching and data extraction run,
steps.
Conclusio
The studies have described methods to
tehxetrsaec t data elements, so data extraction for  n
systematic reviews outlines previously reported  
methods to categorize sentences containing some  
of the data extraction elements.
Data extraction approaches may serve as checks
for currently manua data extraction, 
conducted  then serve l    
atochievveeridfy by a single revmiewaneur,a lthedna tbeceoxmtrea cthioe  
primary source for data element eaxtractionn that a  
person will check, and finally full data extraction to  
allow live systematic reviews.
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